| 研究実績の概要 |
I have focused on the Breast cancer (BC) occurrences through the somatic mutations in the TCGA dataset. I utilized data-intensive informatics methods to elucidate new principles underlying the interpretation of breast cancer sequence variation in a phenotyped population. We have attempted to identify and characterize significant sequence variants on specific biological functions extensively, through mining from mutations in genome sequences of breast tumors.
We unraveled the link of exonic somatic mutations and their combinations to clinical phenotypes, particularly those in Triple-negative patients to other clinical phenotypes. Three levels of features were considered in the clustering analysis: 1) mutation-wise defines the original SNP in respect to the location and nucleotide (A,C,T or G), 2) gene-wise indicates whether the gene has any mutation or not and lastly 3) pathway-wise indicates whether a specific pathway has a mutation or not. Overall, the mutation-wise and gene-wise analysis are consistent, yet the gene-wise analysis is more informative due to its feature matrix being less sparse. The pathway-wise feature help associate the downstream effect of the somatic mutations in the development of the breast cancer. More importantly, the gene-wise mutation also found a combination of TP53 & PIK3CA as significant, in all methods, but in a small sample size, 4.3% (9/208) of the general population of BC patients. We believe this pattern is critical to unravel the complexity of the somatic mutation influence in BC and should warrant further investigation.
|
| 今後の研究の推進方策 |
Previous publications justifying the importance of the BRCA in the breast cancer has warranted us to believe that the somatic mutations in breast cancer can be associated to its clinical phenotypes (ER+/PR+, Her2+, Triple-Negative (TN)). I will continue to investigate this mutational link to the clinical phenotypes and will submit the manuscript accordingly to the results.
|
